https://orcid.org/0009-0004-1121-3332
Figures
Abstract
As modern power systems continue to be integrated with renewable energy sources, the ability to detect faults in these systems quickly and accurately is becoming more sophisticated. A Hybrid Quantum Classical Neural Network (HQCNN) model is presented in this paper that addresses the real-time fault detection challenge in transmission systems. A classical feature extractor, in this case a 1D CNN, and a quantum circuit are combined by the model, which aids in the classification of faults. Evaluations were conducted on simulated IEEE 14 and 39-bus system and on approximately 800 real PMU fault events. An accuracy of 96.43% on simulated data and 94.74% on real PMU data was achieved by the model, outperforming traditional deep learning models and maintaining detection times under 3 milliseconds. Various kind of fault like SDL (single line to ground), DL (double line), TP (three phase), and high-impedance faults, were correctly classified by the system. In a study in which elements of the model were removed, the quantum layer was found to be very important for improved performance. Issues like hardware limits and quantum noise were also looked at. As for the future, larger PMD data sets will be worked on, model explainability will be improved with hybrid XAI methods, and smaller HQCNN models will be developed for use in substation edge devices.
Citation: Hashmi H, Kumar A, Kuttan SR, Moolchandani J, Goel P, Smerat A, et al. (2026) Hybrid quantum-classical neural networks for real-time fault detection in power systems. PLoS One 21(6): e0349887. https://doi.org/10.1371/journal.pone.0349887
Editor: Palaniyappan Sathyaprakash, Maha Bharathi Engineering College, INDIA
Received: August 10, 2025; Accepted: April 14, 2026; Published: June 18, 2026
Copyright: © 2026 Hashmi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The dataset is openly available at www.kaggle.com/datasets/programmer3/pmu-dataset-for-power-line-fault-detection.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Power systems and electric cars are being implemented on the modern power grid systems. As they are being implemented, clean energy and decentralized generation are being supported by these changes, which are also creating new issues. From these challenges, one issue is related to the quick and precise detection of faults that need to be ensured. Some other Issues like short circuits, line-to-ground problems, and equipment failures can cause wide-scale outages if not attended in time [1]. These related troubles are often encountered by traditional protection methods that use fixed thresholds values and perform impedance calculations in modern grids, which outputs high variability, noise, and complex network structures. Hence, there is a need to utilize machine learning techniques to overcome these problems. In the case of faults, deep learning has shown improvements in detection, through which complex patterns are learned from the traces of voltage and current signals [2,3]. Some models like CNNs and LSTMs, are used often which are performed very well in classification and time-based analysis [4]. However, large, labeled data sets are usually required by these models, long training times are reported, and a great need for computing power is present, which in turn makes them a hard sell for real-time or edge-based applications [5].Additionally, many of the deep learning models function like “black boxes,” which does not provide much insight into the decision-making process [6,7].
- I. Quantum Computing: A New Paradigm
A new way to address complex, high-dimensional learning problems is offered by quantum computing using quantum mechanical effects such as entanglement, superposition, etc. [8]. Significant speedups in data processing are potential achievements of QML(Quantum Machine Learning), especially for tasks like clustering, classification, optimization, etc. [9]. However, major limitations are still present in present quantum hardware that we have known as NISQ devices that includes a fixed count of qubits, imperfect gate fidelity, and high sensitivity to noise [10,11]. Because of these constraints, full practicality for deployment in real-world fault detection systems is not yet achieved by fully quantum models.
- II. Hybrid Quantum Classical Neural Networks
To overcome the hardware limitations and still benefiting from its strengths, an HQCNNs (Hybrid Quantum-Classical Neural Networks) is proposed. Classical components such as CNNs (convolutional neural networks) are combined with parameterized quantum circuits (PQCs) to improve feature learning and classification. More accurate data processing is achieved by using the power of both classical and quantum models with current hybrid approach. High accuracy, fast response, and efficient computation are offered by it, making perfect smart grid system to achieve real time fault detection.
- III. Objectives and Contributions
An HQCNN(Hybrid Quantum Classical Neural Network) is designed for real-time fault diagnosis in power grid systems is proposed in this study. Quantum computing layers are incorporated into a conventional deep learning architecture by the model, allowing complex patterns in transient electrical signals to be effectively captured while maintaining reasonable computational cost. The primary contributions of the paper are:
- Designing, implementation of an HQCNN for fault classification using voltage and current waveform data.
- Development of a simulation environment using IEEE 14-bus and 39-bus systems to generate labeled fault data.
- Performance benchmarking of the proposed HQCNN model against conventional LSTM, CNN models in terms of and computational efficiency, accuracy, and latency.
- Analysis of quantum circuit configurations and encoding methods to evaluate the trade-offs between model accuracy and quantum resource requirements.
- IV. Paper Structuring
The work is drafted by section 1 is itself having the introduction, followed by Section 2, that is dedicated for related work on classical and quantum approaches to fault detection. Next, in section 3, the proposed hybrid model and data processing techniques are elaborated. Following section 4 describes various simulation setup, evaluation process. Finally, section 5 showcases conclusion and discussion of the results.
2. Related work
Accurate and real-time fault detection in power systems is regarded as a critical component of intelligent grid operation. Prior research is categorized in this section into four main areas: traditional protection techniques, Quantum Machine Learning applications, classical and deep learning-based fault diagnosis, hybrid quantum classical neural networks, etc.
2.1. Conventional fault detection techniques
Traditional methods for fault detection are included by over current protection, distance relays, differential protection, and impedance-based techniques [12,13]. Fixed thresholds are relied upon by these systems and manual configuration for each network topology is often required, which can be error-prone under dynamic grid conditions. To improve fault localization, time-frequency analysis, noise tolerance methods such as the transform using wavelet [14], the S-transform [3], and Hilbert-Huang Transform (HHT) [15] have been introduced. Although better transient feature extraction is offered by these methods, performance still degrades in noisy environments and careful tuning is required. Mathematical morphology [16] and Kalman filtering [8] have been explored by some studies as signal enhancement tools for improving the reliability of traditional detection frameworks. However, rule-based characteristics remained by these approaches and adaptability to grid reconfigurations, or the stochastic nature of RES is often lacking.
2.2. Deep learning, machine learning approaches
New avenues for data-driven fault detection have been opened by machine learning (ML). Statistical and spectral features extracted from voltage and current signals have been widely applied using SVMs (Support Vector Machines), Random Forests and Decision Trees [9,17]. This capability is further enhanced by deep learning (DL) through the automatic learning of hierarchical features. Spatial pattern recognition in waveform data has been achieved using CNNs [18], while time dependencies in transient responses have been effectively captured by LSTMs [19]. Hybrid CNN-LSTM architectures have been developed by several researchers to leverage the strengths of both models [20,21]. Anomaly detection and data augmentation in imbalanced fault datasets have also been explored using Autoencoders and Generative Adversarial Networks (GANs) [22,23]. Despite their strong performance, substantial computational power and large training datasets are typically demanded by deep learning models, which limit their feasibility for real-time edge-based applications. Furthermore, challenges in safety-critical domains such as power system protection are posed by the opaque nature of these models. While approaches such as attention mechanisms, explainable AI have been proposed for improving the transparency, uncommon remains their practical deployment in operational systems [24,25].
2.3. Power systems using quantum machine learning
QML has been emerged as a useful approach for handling high-dimensional complex problems, including tasks such as classification, regression, and clustering. Potential advantages over classical techniques in specific cases have been shown by several quantum algorithms, including variational quantum classifiers, quantum support vector machines, and quantum kernel methods [26]. Within energy systems, QML has been explored for applications such as load prediction, energy usage analysis, and power flow optimization. An underexplored area remains fault detection, though the feasibility of using quantum algorithms to classify transient waveforms has been demonstrated in initial efforts. Due to the limitations of NISQ devices like qubit decoherence and limited circuit depth, fully quantum models are not yet considered suitable for real-time grid applications [27]. As a result, a shift towards hybrid approaches is being seen in research.
2.4. Hybrid quantum classical neural networks
Classical deep learning components are integrated with quantum layers—usually parameterized quantum circuits (PQCs)—in hybrid quantum-classical neural networks (HQCNNs) [28,29]. Quantum-enhanced feature mapping is allowed by these architectures while classical processing is retained for scalability and stability. Circuit-centric quantum classifiers and quantum convolutional networks (QCNNs) have been successfully applied to image recognition, cybersecurity, and finance [30,31]. Hybrid quantum models have also been explored in small-scale power systems for short-term load forecasting and anomaly detection [32]. This paper aims to fill that gap by designing and evaluating a HQCNN model that classifies fault types based on transient waveform features using a simulation-based dataset from IEEE test systems. Table 1 depicts the summary of various literature Review on Fault Detection in Power Systems.
Despite advancements in fault detection, key research gaps persisted in, including the lack of real-time QML applications (G1), limited use of hybrid quantum-classical models in power systems (G2), no benchmark comparisons with deep learning models (G3), poor model explainability (G4), and reliance on simulated rather than real-world data (G5). The addressing of these gaps is required through innovative integration of QML and classical models for improved real-time fault detection in power systems. Table 2 highlights the research gaps identified in literature.
A comparison of the performance of different fault detection approaches is presented by Tables 3 and 4. Well in controlled environments, conventional methods, including neural networks and wavelet-based techniques, are performed, but noise and complex fault conditions encountered in real-world systems are often failed to be handled. Higher accuracy for both temporal and spatial data is achieved by more advanced models, such as CNNs and LSTMs; however, increased computational demand is incurred by this improvement. Promising accuracy (up to 95%) is shown by quantum-based methods like the Variational Quantum Classifier (VQC), but limitations in scalability and dataset size are faced. Tables 3–5 provide a structured comparison of fault detection approaches, dataset characteristics, and methodological capabilities. A summary of quantitative performance metrics reported in prior studies and in the proposed method is presented in Table 3. A qualitative comparison of classical, deep learning, and hybrid quantum–classical approaches based on reported capabilities in the literature is provided in Table 4. The datasets used in this work, including their source, fault types, and overlap status, are described in Table 5.
Table 5 is emphasized by how both the type and size of datasets influence the evaluation of fault detection models. Simulated data is relied upon by many studies, which allows a wide range of fault patterns to be learned by models such as neural networks and CNNs. In contrast, smaller real-world datasets are used by some approaches, including wavelet-based methods and support vector machines, leading to more reliable and realistic performance assessments. Very limited sample sizes especially constrain quantum models, such as variational quantum classifiers. Overall, the major challenge in accurately evaluating model robustness and generalization capability is posed by the scarcity of real-world data.
In Table 5, the term overlap is referred to by the authors as relating to whether the same fault events or data samples have appeared across different studies or dataset partitions. In addition, the performance of fault detection methods is compared across various fault categories in Table 6. Transient faults are well identified by neural networks and wavelet-based approaches, while strong results for short-circuit fault detection are shown by CNNs. Ground faults are particularly well modeled by LSTMs because of their ability to capture temporal patterns, and robust performance is demonstrated by hybrid CNN–LSTM models when mixed fault conditions are dealt with. Moderate performance across fault types is shown by quantum-based models, such as Quantum CNNs, but hardware limitations and limited dataset availability currently constrain their effectiveness.
3. Methodology
The suggested framework for real-time fault detection in power systems is utilized by an HQCNN (Hybrid Quantum-Classical Neural Network), which integrates with deep learning for extracting the features and a quantum variational circuit for classification, as illustrated in Figs 1–3. The shortcomings of entirely classical models regarding both accuracy and inference speed are aimed to be overcome by this architecture, especially in the identification of transient faults within high-resolution time-series data.
3.1. Framework architecture
The HQCNN architecture consists of three major components: (i) a classical feature extraction module using either a 1D CNNor a BiLSTM network, (ii) a classical-to-quantum encoding layer, and (iii) a Variational Quantum Classifier (VQC).
Given a raw signal , where, n is used as the count of sensors and t is the term used to show the length of the time window, the objective is to predict fault classes y∈
through a non-linear mapping composed of classical and quantum transformations. The overall formulation is given as:
where is the classical feature extraction function,
denotes the quantum encoding, and
refers to the quantum variational circuit. The function
represents the softmax operator that transforms the quantum measurement probabilities into a probabilistic class prediction.
The input signal , representing a segment of voltage or current waveforms from multiple sensors, is initially processed by a classical feature extraction function
, implemented using either a 1D Convolutional Neural Network (CNN) or a Bidirectional Long Short-Term Memory (BiLSTM) network. The extracted features are mapped to a quantum state through an encoding function
and processed by a variational quantum circuit (VQC).The final output y^ is calculated through a Softmax activation as:
3.2. Data collection and preprocessing
For testing the suggested model, simulated and actual data were utilized. Simulated datasets have been created based on the IEEE 14-bus test system through MATLAB/Simulink under different fault scenarios such as single-line-to-ground (SLG), double-line (DL), and three-phase (TP) faults. Actual data was obtained from PMU/SCADA logs of open-access sources such as EPRI and LARIAT. The training and testing dataset includes both simulated and actual data. Simulated fault cases were created based on the IEEE 14-bus and 39-bus test systems, including single-line-to-ground (SLG), double-line (DL), and three-phase (TP) faults with different fault resistances and inception angles. The dataset in the real world includes about 800 events gathered from Phasor Measurement Units (PMUs) and SCADA logs, retrieved from open-source sites like EPRI and LARIAT. These field occurrences comprise events of single-line-to-ground, double-line, three-phase, and high impedance faults (HIF). Simulated data were neat and well-synchronized at 50/60 Hz sampling, whereas field data needed extra steps of preprocessing.The simulated datasets and real-world PMU/SCADA datasets are non-overlapping; training and controlled evaluation were conducted using simulated data, while validation and testing were carried out using real-world events only. All real-world PMU/SCADA data used in this study were obtained from publicly accessible repositories associated with smart grid monitoring projects, and no restricted or proprietary datasets were accessed. Wavelet-based denoising was done more aggressively to counteract ambient noise, event synchronization was also fixed by cross-correlation methods, and missing data in short bursts (less than 20 ms) were filled linearly for maintaining temporal coherence.
Following preprocessing, fixed length overlapping windows were created from the synchronized voltage and current signals to capture localized fault dynamics. To prevent information leakage, data splitting was conducted at the fault-event level, ensuring that all windows derived from a given event were assigned exclusively to either the training, validation, or test set.
3.3. Data preprocessing and feature normalization
As signal quality is important aspect, for this, several important steps taken here for data preprocessing to ensure consistent signal quality. Initially, wavelet-based denoising is applied to detect and remove high-frequency components that were not related to fault events. Then the signals were divided into fixed 5-cycle segments to keep the input size balanced and uniform. At the end, Min–Max normalization was used to scale down all features to the range of 0–1, to make the data suitable for quantum encoding.
During preprocessing, the x is used as raw signal and it is cleaned from noise by applying Discrete Wavelet Transform, in which it is broken down into approximation coefficients named Aj and detail coefficients named Dj in eq. 3.
To enhance the models ability to generalize across different grid scenarios, preprocessing was applied. First, the signal is denoised using Discrete Wavelet Transform (𝐷𝑊𝑇), where a signal (𝑡) is decomposed into approximation 𝐴𝑗(𝑡) and detail coefficients 𝐷𝑗(𝑡).
Denoising is performed by thresholding and reconstructing the signal via Inverse
After denoising, signals are normalized between [0,1] using Min-Max normalization.
To avoid information leakage and preserve event-level amplitude characteristics that are critical for fault discrimination, Min–Max normalization statistics is computed once on training dataset independently. It is done for each signal channel. The same normalization parameters are then applied unchanged to the validation and test datasets. Here, Normalization is not performed on a per-window basis. The data is then segmented into fixed-size windows of 5 cycles (100 ms for 50 Hz systems), with each segment forming an input sample.
Dataset Composition and Split: The complete dataset consists of both simulated and real-world fault events. Simulated data were generated using the IEEE 14-bus and 39-bus test systems, while approximately 800 real-world fault events were collected from PMU/SCADA repositories. The dataset includes balanced representations of SLG (singlelinetoground), DL (double-line), and TP (three-phase) faults to avoid class bias. The combined dataset was divided into 70%, 15%, and 15% for training, validation and testing, respectively, at the fault-event level to prevent information leakage. The same data split was used to generate the results reported in Fig 6 and Table 7.
The x-axis is showing the predicted fault classes, whereas the y-axis is showing the true fault labels.
3.4. Classical feature extraction
The traditional component of the HQCNN is responsible for extracting high-level features from the pre-processed input. Two network architectures were considered as alternatives. The first one employs a 1D CNN consisting of convolutional layers with ReLU activations, max pooling, and fully connected layers. This configuration effectively extracts spatial correlations in waveform patterns. The second setup utilizes a BiLSTM to identify temporal dependencies that are of particular importance when detecting the propagation and evolution of faults over time.
The feature extractor F(X) is either a 1D CNN or a BiLSTM network. For CNN, the output of a convolutional layer is given as:
where is the filter weight at layer
,
is the bias, and
is a non-linear activation such as ReLU. In BiLSTM, the hidden state at time
is updated by combining forward
and
backward states:
The final output vector encapsulates temporal-spatial information and is passed to the quantum encoder.
3.5. Classical-to-quantum encoding
For quantum encoding, each classical feature is mapped to a qubit’s rotation angle using the
gate:
The encoded quantum state is the tensor product across all qubits. For enabling quantum processing, the classical vector is encoded into a quantum state through angle encoding (also known as parameterized rotation encoding). Every scalar zi is translated into the angle for rotation of a quantum gate. The classical output
is encoded through quantum states using angle encoding:
This centred angle encoding preserves sign information and improves the expressive capacity of shallow variational quantum circuits while remaining compatible with NISQ hardware constraints.
The complete quantum state is:
This process yields a multiqubit entangled input state for the variational quantum circuit. The quantum variational circuit computes the expectation value of Pauli-Z operators on every qubit:
The resulting expectation values are passed through the Softmax function to achieve the various class probabilities:
The network is trained by the cross-entropy loss for minimization:
where is used to represent the true labels,
is showing the predicted class probabilities. A hybrid optimization procedure jointly updates classical parameters
and quantum parameters
by minimizing:
where controls the regularization strength on quantum parameters.This unified loss function is used throughout the hybrid training process to jointly optimize classical and quantum parameters.
3.6. Variational quantum classifier (VQC)
The quantum classification phase employs a parameterized quantum circuit to be run on the encoded input. The quantum variational layer employed in this work runs on four qubits, providing adequate expressiveness with compatibility for existing NISQ hardware. The quantum circuit depth is fixed at three layers, where every layer consists of parameterized single-qubit rotations and entangling operations. Entanglement is introduced through Controlled-Z (CZ) gates between neighbouring qubits following each rotation block. Controlled-Z (CZ) gates were chosen for entanglement as they preserve the computational basis structure and are natively supported on several quantum hardware platforms, while offering equivalent entangling capability to CNOT gates for variational circuits. This entanglement is used to preserve non-linear correlations between features in an efficient manner. To reduce the effect of noise byhardware, simple measurement error mitigation procedures were utilized using Qiskit Ignis calibration routines. Only measurement (readout) error mitigation using Qiskit Ignis calibration routines was applied; no full quantum error correction codes were implemented.
Furthermore, the Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer was chosen to train the quantum parameters, due to its stability in noisy environments and effectiveness in approximating gradients from sparse quantum measurements. Each variational layer consists of trainable singlequbit rotation gates applied to all qubits, followed by Controlled-Z (CZ) gates between adjacent qubits to introduce entanglement. The rotation angles
are optimized during training using the SPSA optimizer as part of the hybrid quantum–classical learning process.
The quantum circuit consists of repeating layers of singlequbit parameterized rotation gates and entanglement gates (e.g., CZ or CNOT). The variational circuit output is a probability distribution over measurement outcomes ∣y⟩, for which expected value for a Pauli-Z observable on qubit i is:
The expectation values obtained from the variational quantum circuit are passed through a softmax function to produce class probabilities. Model training is performed by minimizing the unified cross-entropy objective defined in Equation (13), with quantum parameters optimized using the SPSA algorithm.
3.7. Hybrid training and optimization strategy
The HQCNN model got trained end-to-end by a hybrid optimization approach. Classical parameters (CNN/BiLSTM weights) are updated using stochastic gradient descent, while quantum parameters are optimized on simulators or real quantum hardware via the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Let denote the classical parameters and
the quantum parameters. Training is performed by minimizing the unified objective function defined in Equation (13), which jointly optimizes both classical and quantum components with a regularization term controlling the contribution of the quantum parameters.
3.8. Evaluation setupand evaluation metrics
The proposed HQCNN model was developed using PyTorch for the classical deep learning components and Qiskit Machine Learning for implementing the variational quantum circuit. Experiments were carried out on a high-performance computing system equipped with an Intel Xeon processor, an NVIDIA RTX 4090 GPU, and 64 GB of memory, which supported efficient execution of both classical and quantum operations.
The variational quantum circuit is built from repeated layers of parameterized single-qubit rotation gates and Controlled-Z (CZ) entangling gates, as outlined in Section 3.5. Expectation values of Pauli-Z operators are computed using measurement outcomes from the circuit, which serve as inputs for the fault classification task. Common classification metrics, including accuracy, precision, recall, and F1-score, are used to assess model performance, derived from true positive, true negative, false positive, and false negative results. Inference time is also measured to evaluate real-time applicability. All results are reported for each fault category and averaged using five-fold cross-validation to improve reliability and reduce variability.
4. Experimental results and analysis
In this section the performance is analysedfor the HQCNN (Hybrid Quantum-Classical Neural Network) is presented using simulated and real-world datasets. Here, the aim is related to the evaluation of its performance in detecting faults accurately under different operating conditions and to compare it with current classical approaches.
4.1. Performance on simulated data
The performance of the HQCNN against the common CNN, BiLSTM, and shallow quantum-only classifier (QVC) is collated in Table 7. Its counterparts were consistently outperformed by the HQCNN across all metrics.
As Table 7 is showing the best accuracy (96.43%) and F1-score (96.00%) were achieved by the HQCNN, while a competitive inference latency of 2.7 ms was maintained. The observed performance gain is characterized to the ability of the quantum variational layer to model complex non-linear feature interactions through parameterized rotations and entangling operations, which can enhance decision boundaries in ambiguous fault scenarios. Fig 4 is showing Confusion Matrix of HQCNN on Real PMU Data and Fig 5 is showing ROC Curves. Training and Accuracy loss is showing by the Fig 6.
4.2. Fault-type detection accuracy
To further understand model behaviour, the detection accuracy was broken down by fault type. Table 8 presents fault-wise classification results.
4.3. Generalization on real-world data
To evaluate real-world applicability, the model was tested on PMU data collected from an open-access smart grid monitoring project. Table 9 shows the results.
Even under noisy and asynchronous conditions in real-world settings, the HQCNN retained high predictive capability and low inference latency, demonstrating its suitability for deployment in real-time energy management systems. Fig 7 depicts the Inference Latency Comparison Across Models.
4.4. Ablation study
To assess the individual contribution of the quantum layer, an ablation study was conducted. Results are presented in Table 10.In this ablation study, the quantum variational layer was removed and replaced with a fully classical dense layer of comparable size, while keeping all other network components and training settings unchanged. No hyperparameters were tuned during the ablation study; this design isolates the performance impact of the quantum layer itself.
The 3.31% improvement in accuracy confirms that the variational quantum layer adds substantial discriminative power to the overall network.
5. Conclusion and future work
An HQCNN architecture for real-time fault detection in power transmission systems was introduced in this paper. A 1D CNN or BiLSTM-based classical feature extractor was integrated with a four-qubit parameterized quantum circuit (PQC) for classification, with angle encoding and entanglement leveraged via Controlled-Z gates. Complete training of the model was conducted using a hybrid optimization strategy, which combined stochastic gradient descent for classical parameters and the SPSA (Simultaneous Perturbation Stochastic Approximation) algorithm for quantum parameters. Comprehensive experiments were conducted on both simulated datasets generated from IEEE 14-bus and 39-bus systems and real-world Phasor Measurement Unit (PMU) datasets containing approximately 800 real fault events. Fault types evaluated included SLG, DL, TP, and HIF.An overall accuracy 96.43% on simulated data and 94.74% on real-world PMU data was achieved by the HQCNN, with conventional CNN and BiLSTM baselines being outperformed. Inference latency was maintained within 2.7 milliseconds on simulated environments and under 3 milliseconds on real PMU data, with potential for real-time deployment demonstrated. It was confirmed by ablation studies that classification performance was improved by 3.31% over purely classical networks through the inclusion of the quantum variational layer.
Despite the encouraging performance, several challenges remain for practical deployment. Computational resources on edge devices were limited by real-time latency constraints, and widespread adoption continues to be restricted by noise in NISQ (Noisy Intermediate-Scale Quantum) hardware. In addition, although strong robustness was demonstrated by HQCNN, questions related to model interpretability and transparency in fault decision-making remain unresolved. These limitations can be addressed in several ways by future research. First, the proposed HQCNN should be evaluated using larger and more diverse real-world PMU datasets to better assess its generalization across different grid configurations and operating conditions. Second, transparency would be enhanced, operator confidence would be improved, and regulatory requirements would be supported by incorporating explainable AI techniques tailored to hybrid quantum–classical models.Finally, versions of the HQCNN that are simplified and resource-efficient should be designed for deployment on edge devices in substations, ensuring an effective balance between detection accuracy and computational cost.
Finally, the feasibility of HQCNNs as quantum technology matures will be improved through the exploration of more advanced quantum noise mitigation methods and scalable circuit ansatzes.The proposed HQCNN framework is designed and evaluated for single-fault classification scenarios, where one dominant fault type is present at a given time. Simultaneous multi-fault detection was not considered in this study. The framework could be extended to multi-label fault detection by modifying the output layer and loss formulation, which is a potential direction for future work.
References
- 1.
Saadaat H. Power System Analysis. 3rd ed. PSA Publishing; 2010.
- 2.
Glover JM, Overbye T. Power System Analysis and Design. Cengage Learning; 2012.
- 3. Brahmaa S, Girgis AA. Development of adaptive protection scheme of distribution systems with high DER penetration. IEEE Trans Power Del. 2006;21(1):10–7.
- 4. Osowski 4, Huba MT. Fault classification using the support vector machine approach. IEEE Trans Power Del. 2009;24(4):1947–53.
- 5.
Mallat S. A Wavelet Tour of Signal Processing. 3rd ed. Elsevier; 2009.
- 6. Preskill J. Quantum Computing in the NISQ era and beyond. Quantum. 2018;2:79.
- 7. Benedetti M, Lloyd E, Sack S, Fiorentini M. Parameterized quantum circuits as machine learning models. Quantum Sci Technol. 2019;4(4):043001.
- 8. Hashmi H, Dwivedi RK, Kumar A. Identification of Objects using AI & ML Approaches: State-of-the-Art. In: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), MORADABAD, India. 2021. p. 1–5.
- 9. He H, Yan J, Wen J. A comparative study on machine learning techniques for power system disturbance classification. IEEE Trans Ind Informat. 2016;12(3):1136–47.
- 10. Thorp JS, Phadke AG. A new approach to electric power fault detection. IEEE Comput Appl Power. 1996;9(1):39–43.
- 11. Santoso 9 S, Powers EJ, Grady WM, Parsons AC. Power quality disturbance waveform recognition using wavelet-based neural classifier. IEEE Trans Power Del. 2000;15(1):222–7.
- 12. Song YH, Johns AT. Developments in travelling wave protection techniques. IEE Proc Gen Transm Distrib. 1996;143(5):434–41.
- 13. Huang NE, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A Mathematical Phys Eng Sci. 1998;454(1971):903–95.
- 14. Barzegari MR, et al. A new morphological approach for high impedance fault detection. Electr Power Syst Res. 2019;168:159–70.
- 15. Hashmi H, Dwivedi R, Kumar A. YOLO-RS: An Efficient YOLO-Based Approach for Remote Sensing Object Detection. In: 2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART), 2023. p. 50–6.
- 16. Kumar A, Hashmi H, Khan SA, Kazim Naqvi S. SSE: A Smart Framework for Live Video Streaming based Alerting System. In: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), MORADABAD, India. 2021. p. 193–7.
- 17. Wang Y, Zhu M, Shi J. Deep convolutional neural networks for power line fault classification. IEEE Trans Smart Grid. 2019;10(4):4758–68.
- 18. Pourakbari-Kasmaei B. A novel approach for fault classification using LSTM networks. Electr Power Syst Res. 2020;189:106630.
- 19. Hashmi H, Dwivedi R, Kumar A. An AI & ML Based Detection & Identification in Remote Imagery: State-of-the-Art. JAMRIS. 2022;:3–17.
- 20. Farajollahi M, Cotilla-Sanchez E, Kezunovic M. Application of hybrid CNN-LSTM models in fault diagnosis. IEEE Access. 2021;9:65345–55.
- 21. Abdu-Aguye A, et al. Short-circuit fault detection using CNN-LSTM-based learning. IET Smart Grid. 2020;3(2):234–43.
- 22. Dey R, Salem FM. Fault diagnosis using deep autoencoders. In: Proc. IEEE ICMLA. 2017. p. 126–31.
- 23. Haidar MT, et al. Improving grid fault classification with GAN-based data augmentation. IEEE Trans Ind Informat. 2022;18(3):1940–9.
- 24. Havlíček V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, et al. Supervised learning with quantum-enhanced feature spaces. Nature. 2019;567(7747):209–12. pmid:30867609
- 25. Li Q, Wu C. Quantum load forecasting for smart grid applications. IEEE Access. 2022;10:222–34.
- 26. Broughton J, Carminati T, Mezzacapo A. Quantum energy disaggregation. Quantum Mach Intell. 2021;2(1).
- 27. Lu S, Sun L, Zhang H. Quantum neural networks for power grid fault detection. IEEE Trans Smart Grid. 2022;13(6):5123–33.
- 28.
Scchuld M, Petruccione F. Supervised Learning with Quantum Computers. Springer; 2018.
- 29. Chen H, et al. Circuit-centric quantum classifiers for image recognition. Phys Rev A. 2020;101.
- 30. LaRose R, Coyle B. Quantum convolutional neural networks. arXiv preprint. arXiv:1810.08144. 2018.
- 31. Kumar A, Kumar A, Hashmi H, Khan SA. WordPress: A Multi-Functional Content Management System. In: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), MORADABAD, India. 2021. p. 158–61.
- 32. Yilmaz N, Arikan E. Hybrid quantum-classical neural networks for short-term load forecasting. IEEE Access. 2023;11:23001–12.
- 33.
El-Hawary ME. Electric Power Applications of Artificial Intelligence. IEEE Press; 2001.
- 34. Schuld M, et al. Circuit-centric quantum classifiers. Phys Rev A. 2020;101(3).
- 35. Hashmi H, Kumar Dwivedi R, Kumar A. Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA. JAMRIS. 2024;18(2):31–45.
- 36. Im S, Khodaei M, Tong L. Quantum optimization of power flow in large-scale grids. IEEE Trans Smart Grid. 2023;14(1):44–55.
- 37. Adhikari S, Gurung K, Srivastava N. Explainable AI for intelligent power fault analysis. Appl Energy. 2023;330.
- 38. Ray RN, Mahanta AC. A novel SVM-based fault detection scheme for transmission lines. In: Proc. IEEE ICIEA. 2014. p. 376–81.